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Inseed

AI for edtech apps

Edtech apps, made smarter without a rewrite.

We add the AI features that matter in learning products, a study assistant grounded in your content, voice-based practice, instructor content tools, and adaptive paths, to the app you already ship.

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You have a working app. Leadership wants AI in it.

Your app is in production. Learners open it, courses get finished, and the content and progress data have been building up for years. Now leadership wants AI inside it: a tutor that answers a question at 11pm, practice a learner can speak to instead of tap through, an instructor tool that drafts a quiz from a lesson. The idea is right. The way most shops respond is not.

Every quote you get back starts with "first we rebuild". The real constraint in edtech is not the model, it is the content library, the progress tracking, and the enrollment and payment flows you already have running and do not want to risk. And there is a second constraint that is specific to learning: an AI tutor that confidently states something wrong teaches the wrong thing. So we do not rebuild, and we do not let the model freewheel. We add AI as a layer beside your content and progress data, grounded in your own material, and the parts of your app that already work stay exactly where they are.

What we build

Four AI features that earn their place in a learning app.

These are the patterns we see land in education products. Each one sits beside your existing content and progress data, grounded in the material you already own rather than the open web.

Study assistant grounded in your content

An in-app assistant that answers a learner's questions about the material, drawn from your own courses, lessons, and docs rather than the open internet, so the answer matches what you actually teach.

What it changes
A learner gets unstuck at the moment they are stuck instead of dropping off, and the answer cites the lesson it came from so they can go read the source.
How it is built
Retrieval over your content library so answers stay inside your material, with a refusal path when a question falls outside what you have taught.

Voice-based practice and tutoring

A spoken practice mode: the learner talks, the app listens and responds in real time, for language practice, reading aloud, interview prep, or a hands-free walk-through of a concept.

What it changes
Practice that needed a human partner or a tutor becomes something a learner can do alone, on their commute, as often as they want.
How it is built
A real-time voice pipeline with streaming speech in and out, tuned for latency so the conversation feels live rather than a walkie-talkie.

Instructor content tools

A drafting layer for the people who build your courses: generate a quiz from a lesson, a summary from a transcript, or a first draft of an explanation, from the source material you already have.

What it changes
The busywork that slows course production drops. An instructor edits a draft instead of starting from a blank page, and the output stays consistent with your format.
How it is built
A generation pass over your source content, with the instructor reviewing and correcting before anything is published to learners.

Adaptive paths and feedback

Recommending what a learner should do next based on where they are, plus first-pass feedback on open responses so a short-answer question is not a dead end.

What it changes
The path stops being one-size-fits-all. A struggling learner gets the reinforcement and a fast learner skips ahead, and open questions get feedback instead of being ungraded.
How it is built
Progress signals and rules decide the next step; a model provides draft feedback on open responses, with a human-review checkpoint on anything that affects a grade.

Proof

What we have actually shipped in and around edtech.

We name only work we can stand behind, and we are precise about who delivered it. The first three are the learning-product track record; the last two are the AI depth behind the features above.

Kinobody

Built by our founder and team

An influencer-backed course and coaching platform for a fitness creator: sell and deliver video courses, run customized learning paths, and let the team manage it all from an admin portal. Our founder built the mobile apps, the auth and security, and the admin CMS.

  • 100k+ downloads and a ~4.8-star App Store rating (confirmed by Shuhel)
  • Course delivery plus a customizable plan and pathway builder in the admin portal
  • Live on iOS, Android, and web, serving 10k+ monthly users
AI for fitness and wellness apps

College Cost Secrets

Frontend built by our founder and team

A college-planning platform with four role-based portals for families, agents, admission professionals, and admins, including college search and comparison. The same keyword search is exactly where a semantic, natural-language search would slot in as the AI upgrade.

  • College search, favorites, and side-by-side comparison with saved searches
  • Federal Student Aid Index (SAI) calculator and financial plan
  • Four-role RBAC frontend across family, agent, professional, and admin
How we build AI search

BLE Result Processing

Built by our founder

A multi-tenant school result-processing system for municipalities: ingest school data and grade sheets, compute grades and scores, and present results and analytics. Our founder built it full-stack early in his career, one platform serving many municipalities.

  • Subdomain-based multi-tenancy with a separate database per municipality
  • A result-processing engine that turns grade sheets into computed grades and scores
  • Older project with no public adoption metrics; described here from the build, not a live demo

Playlists

Delivered by our team via Toptal

A live app with an AI search that turns a vague natural-language query into results. The same retrieval pattern is what lets a study assistant find the right lesson by meaning instead of by exact keyword.

  • GPT-4o query enrichment plus embedding-based semantic search
  • Staged matching pipeline that always returns usable results
  • Reported +200% growth in AI-driven searches after launch
Read the Playlists case study

Epiphra

Delivered by Inseed, founder as tech lead

A live AI reflection companion where users talk to a real-time voice assistant that remembers past conversations. Not an edtech app, but it is the exact capability an edtech product needs: production voice AI plus long-term semantic memory, shipped to both stores.

  • Real-time bidirectional voice via the Gemini Live API with live transcription
  • Long-term semantic memory via embeddings stored in pgvector and injected into the prompt
  • Live on the App Store and Play Store; our founder led the architecture
How we build voice AI

Straight answer on the gap: we have not yet shipped an LLM tutor inside a live edtech product. What we have shipped is the pieces separately, course and LMS delivery at scale (Kinobody, 100k+ downloads), multi-role and multi-tenant education platforms (College Cost Secrets, BLE Result Processing), and the AI itself in other live apps, real-time voice plus semantic memory in Epiphra and semantic search in Playlists. The audit is where we prove the specific feature on your app, in a throwaway prototype, before you commit to a build.

Borrowed proof, labeled honestly

React Native holds up in education at scale.

Khan Academy has run React Native in production to deliver learning to millions, and content-heavy consumer apps like Bloomberg and Shopify run on it at scale. These are not our clients. They are proof that the framework we build on already carries content-heavy products used by large audiences, so the question for yours is the feature, not the foundation.

How it starts

It starts with an audit, not a rebuild.

We do not open with a six-figure proposal. We open with one week of work that tells you exactly where AI fits in your app, what it costs to run, and whether it is worth building at all.

Step 1

Mobile App Audit

$5K to $8K

1 week

A written assessment of where AI fits in your app, a working proof-of-concept of the top feature, and a board-ready cost model. The fee credits toward a Sprint signed within 30 days, or is refunded if the report is not useful.

Step 2

AI Integration Sprint

$15K to $30K

2 to 4 weeks

One AI feature shipped into your existing app, as a thin integration layer with fallbacks, caching, and guardrails. Your architecture stays yours.

Step 3

AI-Native Mobile MVP

$25K to $75K

6 to 12 weeks

A production-ready AI-native mobile MVP when the answer is a new product rather than a feature. Fixed price, fixed milestones, both stores.

See full pricing

FAQ

Edtech questions, answered straight.

How do you stop an AI tutor from teaching something wrong?

By grounding it in your content and letting it refuse. The assistant answers from your own courses and lessons rather than the open web, cites the source it drew from, and is built to say it does not know when a question falls outside your material. A tutor that invents a confident wrong answer is worse than no tutor, so the grounding and the refusal path are the feature, not an afterthought.

Can you add AI without rebuilding our content library or LMS?

Yes. The AI layer reads from the content and progress data you already have rather than replacing it. Your courses, your enrollment, and your existing learners stay where they are; the assistant, the practice mode, and the instructor tools sit beside them and feed on what you already own.

Is the voice practice good enough to feel like a real conversation?

That depends on latency, and latency is the thing we tune for. We have shipped real-time voice in Epiphra using a streaming speech-in, speech-out pipeline so the exchange feels live rather than a delayed walkie-talkie. For practice modes, that responsiveness is what makes a learner keep going instead of giving up on it.

Will AI grading replace our instructors?

No, it feeds them. We build first-pass feedback on open responses so a short-answer question is not a dead end, but a human-review checkpoint sits on anything that affects a grade. Your instructors keep the final call; the model just removes the blank-page and the ungraded-backlog problems.

How do you handle student data and minors when a prompt goes to an LLM?

Carefully, and with the data path logged. We send only what the feature needs, prefer a no-training endpoint under contract, and keep personal identifiers out of the prompt where the feature does not need them. If your product serves minors and falls under COPPA or FERPA, we scope the data flow around that from the start rather than bolting it on, and we tell you plainly what we can and cannot attest to.

Is React Native robust enough for a content-heavy learning app?

Yes, and we can point at live evidence. Kinobody delivers video courses and customized plans across iOS, Android, and web with 100k+ downloads. Epiphra runs real-time voice in production. Beyond our work, Khan Academy has run React Native to deliver learning at scale, and Bloomberg runs a content-heavy consumer app on it.

Ready to add AI to your edtech app?

30 minutes, free. We will look at your app and tell you honestly where AI fits, what it costs, and what we would build first. Shuhel will be on the call.

Book a 30-minute call